弹药集装单元储存静力学有限元分析

以简单的六面体结构为基础,以“铝板-聚氨酯泡沫材料-铝板”复合板材为弹药集装单元的主体材料,结合弹药集装单元的承载能力要求,建立弹药集装单元的基本模型。通过其在储存状态下的静力学有限元分析,了解3种不同堆垛情况下,弹药集装单元的应力与应变情况,为进一步研究弹药集装单元结构强度提供理论依据。     

加权主成分TOPSIS法的防空导弹型号论证决策

针对防空导弹型号论证决策问题,讨论了影响型号论证的主要因素,建立了基于加权主成分TOPSIS法的防空导弹型号论证决策模型,实现了对新研制型号方案的综合评价和分析.在此基础上,通过实例验证了方法的实用性与有效性.论文的研究成果为防空导弹型号论证提供了辅助决策方法和智力支持.     

采用弹道修正技术的红外干扰弹性能优化

针对弹道修正弹的高维非线性特性导致的性能优化难题,改变概念设计阶段传统的串行设计方式,提出了一种基于实验设计(Design Of Experiments,DOE)和响应面(Response Surface Methodology,RSM)的智能优化算法,定义基本的弹丸结构模型以及相关的设计参数.在DOE的基础上,将设计...     

探空火箭级间段的CAD系统

本文在CADKEY绘图软件包的支持下,开发了加筋圆柱结构CAD系统—Hcadx.此系统通过菜单引导用户的操作,可用经验公式或解析法作轴压稳定分析,用罚函数法优化参数,可以在不退出Hcadx的情况下启动CADKEY,自动绘制设计草图。此草图经过适当地编辑、修改、便可成为工程中能接受的工作图纸。     

Equity in genetic newborn screening

State‐level newborn screening allows for early treatment of genetic disorders, which can substantially improve health outcomes for newborns. As the cost of genetic testing decreases, it is becoming an essential part of newborn screening. A genetic disorder can be caused by many mutation variants; therefore, an important decision is to determine which variants to search for (ie, the panel design), under a testing budget. The frequency of variants that cause a disorder and the incidence of the disorder vary by racial/ethnic group. Consequently, it is important to consider equity issues in panel design, so as to reduce disparities among different groups. We study the panel design problem using cystic fibrosis (CF) as a model disorder, considering the trade‐offs between equity and accuracy, under a limited budget. Most states use a genetic test in their CF screening protocol, but panel designs vary, and, due to cost, no state's panel includes all CF‐causing variants. We develop models that design equitable genetic testing panels, and compare them with panels that maximize sensitivity in the general population. Our case study, based on realistic CF data, highlights the value of equitable panels and provides important insight for newborn screening practices.     

重力势能锁止型外骨骼设计及其效能评价

现存的被动式外骨骼存在助力效果不明显等缺陷。基于人体步态特征,提出了将人体行走过程中的重力势能转化为人体抬腿过程中动能的原理,通过弹簧、曲柄滑块机构、棘轮棘爪机构以及链传动机构的组合,设计了一款被动式下肢机械外骨骼,并通过外骨骼动力学分析和生理消耗指数实验测试的方法进行了外骨骼性能评价。动力学分析结果表明,穿戴外骨骼后髋关节峰力矩总量降低了23.43%,髋关节做功总量降低了30.59%,初步验证了外骨骼的有效性。生理消耗指数实验测试结果表明, 穿戴外骨骼对于短距离动作转换未产生显著效果,但在长距离行走和上坡行走中分别降低了8.1%和10.4%的能量消耗。因此,该下肢外骨骼助力性能得到了验证,为相关领域的研究提供了实例化参考。     

Multi-objectives nonlinear structure optimization for actuator in trajectory correction fuze subject to high impact loadings

This paper presents an actuator used for the trajectory correction fuze, which is subject to high impact loadings during launch. A simulation method is carried out to obtain the peak-peak stress value of each component, from which the ball bearings are possible failures according to the results. Subsequently, three schemes against impact loadings, full-element deep groove ball bearing and integrated raceway, needle roller thrust bearing assembly, and gaskets are utilized for redesigning the actuator to effectively reduce the bearings' stress. However, multi-objectives optimization still needs to be conducted for the gaskets to decrease the stress value further to the yield stress. Four gasket's structure parameters and three bearings' peak-peak stress are served as the four optimization variables and three objectives, respectively. Optimized Latin hypercube design is used for generating sample points, and Kriging model selected according to estimation result can establish the relationship between the variables and objec-tives, representing the simulation which is time-consuming. Accordingly, two optimization algorithms work out the Pareto solutions, from which the best solutions are selected, and verified by the simulation to determine the gaskets optimized structure parameters. It can be concluded that the simulation and optimization method based on these components is effective and efficient.     

MF-CFI: A fused evaluation index for camouflage patterns based on human visual perception

The evaluation index of camouflage patterns is important in the field of military application. It is the goal that researchers have always pursued to make the computable evaluation indicators more in line with the human visual mechanism. In order to make the evaluation method more computationally intelligent, a Multi-Feature Camouflage Fused Index (MF-CFI) is proposed based on the comparison of grayscale, color and texture features between the target and the background. In order to verify the effectiveness of the proposed index, eye movement experiments are conducted to compare the proposed index with existing indexes including Universal Image Quality Index (UIQI), Camouflage Similarity Index (CSI) and Structural Similarity (SSIM). Twenty-four different simulated targets are designed in a grassland background, 28 observers participate in the experiment and record the eye movement data during the observation process. The results show that the highest Pearson correlation coefficient is observed between MF-CFI and the eye movement data, both in the designed digital camouflage patterns and large-spot camouflage patterns. Since MF-CFI is more in line with the detection law of camouflage targets in human visual perception, the proposed index can be used for the comparison and parameter optimization of camouflage design algorithms.     

Damage modeling of ballistic penetration and impact behavior of concrete panel under low and high velocities

This work presents a numerical simulation of ballistic penetration and high velocity impact behavior of plain and reinforced concrete panels. This paper is divided into two parts. The first part consists of numerical modeling of reinforced concrete panel penetrated with a spherical projectile using concrete damage plasticity (CDP) model, while the second part focuses on the comparison of CDP model and Johnson-Holmquist-2 (JH-2) damage model and their ability to describe the behavior of concrete panel under impact loads. The first and second concrete panels have dimensions of 1500 mm × 1500 mm × 150 mm and 675 mm × 675 mm × 200 mm, respectively, and are meshed using 8-node hexahedron solid elements. The impact object used in the first part is a spherical projectile of 150 mm diameter, while in the second part steel projectile of a length of 152 mm is modeled as rigid element. Failure and scabbing characteristics are studied in the first part. In the second part, the com-parison results are presented as damage contours, kinetic energy of projectile and internal energy of the concrete. The results revealed a severe fracture of the panel and high kinetic energy of the projectile using CDP model comparing to the JH-2 model. In addition, the internal energy of concrete using CDP model was found to be less comparing to the JH-2 model.     

Real-time prediction of projectile penetration to laminates by training machine learning models withfinite element solver as the trainer

Studies on ballistic penetration to laminates is complicated, but important for design effective protection of structures. Experimental means of study is expensive and can often be dangerous. Numerical simu-lation has been an excellent supplement, but the computation is time-consuming. Main aim of this thesis was to develop and test an effective tool for real-time prediction of projectile penetrations to laminates by training a neural network and a decision tree regression model. A large number of finite element models were developed;the residual velocities of projectiles fromfinite element simulations were used as the target data and processed to produce sufficient number of training samples. Study focused on steel 4340tpolyurea laminates with various configurations. Four different 3D shapes of the projectiles were modeled and used in the training. The trained neural network and decision tree model was tested using independently generated test samples using finite element models. The predicted projectile velocity values using the trained machine learning models are then compared with thefinite element simulation to verify the effectiveness of the models. Additionally, both models were trained using a published experimental data of projectile impacts to predict residual velocity of projectiles for the unseen samples. Performance of both the models was evaluated and compared. Models trained with Finite element simulation data samples were found capable to give more accurate predication, compared to the models trained with experimental data, becausefinite element modeling can generate much larger training set, and thus finite element solvers can serve as an excellent teacher. This study also showed that neural network model performs better with small experimental dataset compared to decision tree regression model.